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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Impact of Interposer Microstructure on Ionic Transport in Liquid-Phase Bicarbonate Electrolysis

The electrochemical reduction of CO 2 (CO 2 RR) is a potentially scalable approach for converting captured carbon dioxide into value-added products. Conventional gas-phase electrolysis systems can suffer from carbonate crossover, which limits the efficiency of the system. Liquid-phase (bi)carbonate electrolysis using bipolar membrane electrode assemblies (BPMMEA) has emerged as a promising alternative. The interposer layer, a porous mass-transport material between the BPM and the catalyst, is an essential component of the MEA, as it allows evolved CO 2 to reach the catalyst surface for reaction. In the absence of this layer, evolved CO 2 generated by the pH swing process at the BPM can be converted back into (bi)carbonate (CO 2 recapture) due to the high bulk pH. Thus, clear design guidelines are needed to maximize CO 2 conversion, minimize CO 2 recapture in the catholyte, and improve energy efficiency. Here, the transport properties of the interposer are systematically characterized by X-ray tomography and symmetric-cell impedance spectroscopy to quantify porosity, tortuosity, and the resulting MacMullin number. We then examine the correlation between these material properties and the electrolyzer performance. We focus on characterizing two commercial porous membrane filters, mixed cellulose ester (MCE) and poly(ether sulfone) (PES).

CO2 electrolysis↗
DOE OSTI2023

Learning local equivariant representations for large-scale atomistic dynamics

Abstract A simultaneously accurate and computationally efficient parametrization of the potential energy surface of molecules and materials is a long-standing goal in the natural sciences. While atom-centered message passing neural networks (MPNNs) have shown remarkable accuracy, their information propagation has limited the accessible length-scales. Local methods, conversely, scale to large simulations but have suffered from inferior accuracy. This work introduces Allegro, a strictly local equivariant deep neural network interatomic potential architecture that simultaneously exhibits excellent accuracy and scalability. Allegro represents a many-body potential using iterated tensor products of learned equivariant representations without atom-centered message passing. Allegro obtains improvements over state-of-the-art methods on QM9 and revMD17. A single tensor product layer outperforms existing deep MPNNs and transformers on QM9. Furthermore, Allegro displays remarkable generalization to out-of-distribution data. Molecular simulations using Allegro recover structural and kinetic properties of an amorphous electrolyte in excellent agreement with ab-initio simulations. Finally, we demonstrate parallelization with a simulation of 100 million atoms.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Upcycled high-strength aluminum alloys from scrap through solid-phase alloying

Although recycling secondary aluminum can lead to energy consumption reduction compared to primary aluminum manufacturing, products produced by traditional melt-based recycling processes are inherently limited in terms of alloy composition and microstructure, and thus final properties. To overcome the constraints associated with melting, we have developed a solid-phase recycling and simultaneous alloying method. This innovative process enables the alloying of 6063 aluminum scrap with copper, zinc, and magnesium to form a nanocluster-strengthened high-performance aluminum alloy with a composition and properties akin to 7075 aluminum alloy. The unique nanostructure with a high density of Guinier-Preston zones and uniformly precipitated nanoscale η‘/Mg(CuZn) 2 strengthening phases enhances both yield and ultimate tensile strength by >200%. By delivering high-performance products from scrap that are not just recycled but upcycled , this scalable manufacturing approach provides a model for metal reuse, with the option for on-demand upcycling of a variety of metallic materials from scrap sources.

36 MATERIALS SCIENCE↗
NASA NTRS2022

Charging Experiments and Filtration Testing on Facemask Materials: Towards Goals of Full Performance Restoration of Used N95 Masks and Enhancement of Common-Fabric Facemasks

Different techniques have been developed for sanitizing N95 masks while retaining whatever charge remains on them. However, these sanitizing methods do not address the fact that the electrical charge, filtration efficacy, and breathability of such masks may be expected to degrade during long-term use based on mechanisms known in the literature. In this study, approaches to electrically (re)charging facemask filter materials were explored in response to the COVID-19 induced shortage of N95 masks. The effort consisted of charging experiments followed by filtration testing conducted at the coupon level during a month-long period (May 2020). The main focus of the work was to develop a recharging technique which could eventually be adapted and used as part of a broader full-performance restoration process for used N95 masks, e.g., which might employ simple washing by alcohol or soapy water as a preceding step for sanitization and declogging. Most tested coupons were therefore nonwoven meltblown fibrous polypropylene (PP) filter media extracted from decades old N95 masks. Keeping in mind commonly used cloth facemasks, limited trials were also performed with some commercially available fabrics. Charging was done at elevated temperature and was followed by cooling to freeze in the applied charges produced from corona discharges using different electrode configurations and parameter combinations, or from triboelectric rubbing. Charging was also focused on using potentially scalable equipment, e.g., Van de Graaff generators or consumer ionic hair products. Some degree of success was achieved with one of the fabrics and the PP filter media which was first purposefully discharged to have typical filtration efficiency (FE) of ~35%. Numerous attempts at corona charging discharged media resulted in filtration efficiency (FE) ≥ 80 – 85%. Some measure of charge robustness was also established via stable performance during loading testing. However, results where FE ≥ 90% were not reliably repeatable and no results met the FE ≥ 95% goal due to shortcomings of the charging process. The best three trials for PP media statistically met the FE ≥ 90% criteria, amounted to a small percentage of the trials relying on an ionic hair dryer, and are attributed to good embedding of charges via aerodynamic force. Triboelectric charging significantly enhanced the viability of one of the polyester fabrics –measured FE increased from 19% up to 46% while maintaining a reasonably low pressure drop.

N95↗
NASA NTRS2005

Fabrication end Deployment Testing of Meter Solar Sail Quadrants for a Scaleable Square Solar Sail Ground Test System

In order for solar sail propulsion technologies to be considered as a viable option for a wide range of near term practical missions a predictable, stable, reliable, manufactureable, scaleable, and cost effective system must be developed and tested first on earth and then on orbit. The design and development of a Scaleable Square Solar Sail System (S^4) is well underway a t AEC-Able Engineering Co. Inc., and the design and production of the Solar Sails for this system is being carried out by SRS Technologies. In April and May of 2004 a single quadrant 10-meter system was tested at NASA LARC's vacuum chamber and a four quadrant 20-meter system has been designed and built for deployment and testing in the Spring of 2005 at NASA/Glenn Research Center's Plumb Brook Facility. SRS has developed an effective and efficient design for triangular sail quadrants that are supported are three points and provide a flat reflective surface with a high fill factor. This sail design is robust enough for deployments in a one atmosphere, one gravity environment and incorporates several advanced features including adhesiveless seaming of membrane strips, compliant edge borders to allow for film membrane cord strain mismatch without causing wrinkling and low mass (3% of total sail mass) ripstop. This paper will outline the sail design and fabrication process, the lessons learned and the resulting mature production, packaging and deployment processes that have been developed. It will also highlight the scalability of the equipment and processes that were developed to fabricate and package the sails. Based on recent experience, SRS is confidant that flight worthy solar sails in the 40-120-meter size range with areal density in the 4-5g/sq m (sail minus structure) range can be produced with existing technology. Additional film production research will lead to further reductions in film thickness to less than 1 micron enabling production of sails with areal densities as low as 20 g/sq m using the current design resulting in a system areal density of as low as 5.3g/sq m. These areal densities are low enough to allow nearly all of the Solar Sail missions that have been proposed by the scientific community and the fundamental technology required to produce these sails has been demonstrated on the ground test sails that have recently been built. These demonstrations have shown that the technology is mature enough to build sails needed to support critical science missions. Solar Sails will be an enabling technology for NASA's Vision for Space Exploration by allowing communication satellite orbits that can maintain continuous communication with the polar regions of the Moon and Mars and to support solar weather monitoring to provide early warning of solar flares and storms that could threaten the safety of astronauts and other spacecraft.

Laue, Greg↗
DOE OSTI2020

Software-Defined Network for End-to-end Networked Science at the Exascale

Domain science applications and workflow processes are currently forced to view the network as an opaque infrastructure into which they inject data and hope that it emerges at the destination with an acceptable Quality of Experience. There is little ability for applications to interact with the network to exchange information, negotiate performance parameters, discover expected performance metrics, or receive status/troubleshooting information in real time. The work we presen here is motivated by a vision for a new smart network and smart application ecosystem that will provide a more deterministic and interactive environment for domain science workflows. The Software-Defined Network for End-to-end Networked Science at Exascale (SENSE) system includes a model-based architecture, implementation, and deployment which enables automated end-to-end network service instantiation across administrative domains. An intent based interface allows applications to express their high-level service requirements, an intelligent orchestrator and resource control systems allow for custom tailoring of scalability and real-time responsiveness based on individual application and infrastructure operator requirements. This allows the science applications to manage the network as a first-class schedulable resource as is the current practice for instruments, compute, and storage systems. Deployment and experiments on production networks and testbeds have validated SENSE functions and performance. Emulation based testing verified the scalability needed to support research and education infrastructures. Key contributions of this work include an architecture definition, reference implementation, and deployment. This provides the basis for further innovation of smart network services to accelerate scientific discovery in the era of big data, cloud computing, machine learning and artificial intelligence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Software-defined network for end-to-end networked science at the exascale

Domain science applications and workflow processes are currently forced to view the network as an opaque infrastructure into which they inject data and hope that it emerges at the destination with an acceptable Quality of Experience. There is little ability for applications to interact with the network to exchange information, negotiate performance parameters, discover expected performance metrics, or receive status/troubleshooting information in real time. The work presented here is motivated by a vision for a new smart network and smart application ecosystem that will provide a more deterministic and interactive environment for domain science workflows. The Software-Defined Network for End-to-end Networked Science at Exascale (SENSE) system includes a model-based architecture, implementation, and deployment which enables automated end- to-end network service instantiation across administrative domains. An intent based interface allows applications to express their high-level service requirements, an intelligent orchestrator and resource control systems allow for custom tailoring of scalability and real-time responsiveness based on individual application and infrastructure operator requirements. This allows the science applications to manage the network as a first-class schedulable resource as is the current practice for instruments, compute, and storage systems. Deployment and experiments on production networks and testbeds have validated SENSE functions and performance. Emulation based testing verified the scalability needed to support research and education infrastructures. Key contributions of this work include an architecture definition, reference implementation, and deployment. This provides the basis for further innovation of smart network services to accelerate scientific discovery in the era of big data, cloud computing, machine learning and artificial intelligence.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Scalable fabrication of a tough and recyclable spore-bearing biocomposite thermoplastic polyurethane

Thermoplastic polyurethanes (TPUs) are a class of versatile thermoplastic elastomers, but most of their products lack a proper recycling strategy or have no end-of-life solutions. To pursue a sustainable end-of-life solution for TPU-based products, self-disintegrating biocomposite TPUs have recently been developed by embedding spores of TPU-degrading bacteria into TPUs via melt extrusion. Herein, we improve upon spore-bearing biocomposites and demonstrate industrially relevant manufacturing conditions for fabricating biocomposite TPUs. To minimize the coloration of biocomposite TPUs, spore production was modified. The innate brown color of the resulting materials was diminished by reducing FeSO 4 in sporulation media, generating white spores without compromising spore productivity, viability, morphology or heat-shock tolerance. Biocomposite TPUs containing white spores displayed a 45 % increase in toughness compared to TPUs without spores, while retaining ∼ 90 % spore viability post processing. Furthermore, biocomposite TPU fabrication was demonstrated using a scalable continuous extruder followed by injection molding. Biocomposite TPUs generated by these industry-relevant processes exhibited comparable toughness improvement and spore viability to biocomposite TPU prepared using a lab scale microcompounder, while enhancing productivity by 30-fold. Finally, spore addition significantly improved the recyclability of biocomposite TPUs, enabling 80 % toughness retention after 5 rounds of iterative melt processing. Additionally, no negative effect on the lifespan of the generated TPUs was observed over 1 year of storage. Overall, this study confirms that spore-bearing biocomposite TPUs are promising for practical applications, offering an accessible method to enhance toughness and sustainability of commercial TPUs through the incorporation of spore-based living fillers.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Online Event Detection in Synchrophasor Data with Graph Signal Processing

Online detection of anomalies is crucial to enhancing the reliability and resiliency of power systems. We propose a novel data-driven online event detection algorithm with synchrophasor data using graph signal processing. In addition to being extremely scalable, our proposed algorithm can accurately capture and leverage the spatio-temporal correlations of the streaming PMU data. This paper also develops a general technique to decouple spatial and temporal correlations in multiple time series. Finally, we develop a unique framework to construct a weighted adjacency matrix and graph Laplacian for product graph. Case studies with real-world, large-scale synchrophasor data demonstrate the scalability and accuracy of our proposed event detection algorithm. Compared to the state-of-the-art benchmark, the proposed method not only achieves higher detection accuracy but also yields higher computational efficiency.

Event detection↗
DOE OSTI2020

Online Event Detection in Synchrophasor Data with Graph Signal Processing

Online detection of anomalies is crucial to enhancing the reliability and resiliency of power systems. We propose a novel data-driven online event detection algorithm with synchrophasor data using graph signal processing. In addition to being extremely scalable, our proposed algorithm can accurately capture and leverage the spatio-temporal correlations of the streaming PMU data. This paper also develops a general technique to decouple spatial and temporal correlations in multiple time series. Finally, we develop a unique framework to construct a weighted adjacency matrix and graph Laplacian for product graph. Case studies with real-world, large-scale synchrophasor data demonstrate the scalability and accuracy of our proposed event detection algorithm. Compared to the state-of-the-art benchmark, the proposed method not only achieves higher detection accuracy but also yields higher computational efficiency.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Chapter 2: Evaluating a Concentrating Solar Power Plantas an Extended-Duration Peaking Resource

We explore the ability of a concentrating solar power (CSP) plant with thermal energy storage (TES) to provide peaking capacity. We focus on future power systems, wherein net load patterns may be significantly different than they are today (e.g., due to higher renewable-energy penetrations). We examine 28 locations in the southwestern United States over an 18-year period. The hourly operation of the CSP plants are simulated to determine their potential to provide energy during an eight-hour peak-load window for each day up to 365 days per year. Our result shows that for the large majority of locations and years, CSP plants with certain configurations (i.e., in terms of solar field and TES sizes) can provide nearly 100% peak-load capacity. We examine also the amount of supplemental energy (e.g.,by using natural gas as a supplemental thermal-energy source) that would be required to ensure that a CSP plant could serve the eight highest-load hours of every day of the year. We find that in most cases, a CSP plant supplemented with natural gas would require less than 5% of the fuel that is used by a natural-gas fired power plant providing the same level of reliable capacity. A series of sensitivity analyses show that these results are robust to the number of peak-load hours and days that are considered and the configuration of the CSP plant.

capacity value↗
DOE OSTI2025

ROOT RNTuple and EOS: The Next Generation of Event Data I/O

For several years, the ROOT team is developing the new RNTuple I/O subsystem in preparation of the next generation of collider experiments. Both HL-LHC and DUNE are expected to start data taking by the end of this decade. They pose unprecedented challenges to event data I/O in terms of data rates, event sizes, and event complexity. At the same time, the I/O landscape is becoming more diverse. HPC cluster file systems and object stores, NVMe disk cache layers in analysis facilities, and S3 storage on cloud resources are mixing with traditional XRootD-managed spinning disk pools.The ROOT team will finalize a first production version of the RNTuple binary format by the end of 2024. After this point, ROOT will provide backward compatibility for RNTuple data. This contribution provides an overview of the RNTuple feature set, the related R&D activities and the long-term vision for RNTuple. We report on performance, interface design, tooling, robustness, integration with experiment frameworks, and validation results, as well as recent R&D on parallel reading and writing and exploitation of modern hardware and storage systems. We will give an outlook on possible future features after a first production release.Collaboratively, the IT and EP departments at CERN have launched a formal project within the Research and Computing sector to evaluate the novel data format for physics analysis data utilized in LHC experiments and other fields. This part of the project focuses on validating the scalability of the EOS storage backend during the transition from the over 25 years old TTree production format to the newly developed RNTuple format, using both replicated and erasure-coded storage profiles.

Blomer, Jakob [CERN]↗
DOE OSTI2024

A Pro‐Angiogenic Immunoprotective Membrane for Cell Therapies

Abstract Immunoisolation strategies that rely on porous membranes play an important role in cell transplantation therapies to protect cells from the host's immune system. These membranes must possess immunoprotective properties while facilitating the transport of nutrients and cell products to maintain the functional integrity of encapsulated cells. An easy and scalable process is described to fabricate a dual function porous polymeric membrane that shields cells against immune cell attack and promotes vascularization to address the nutritional and oxygen requirements of transplanted cells. The fabrication process results in a membrane cross‐section with a gradient of nanopores to micropores that support cell immunoisolation and interfacial vascularization requirements, respectively. The membranes demonstrate excellent cell compatibility and effectively prevent T cell transmigration without compromising glucose diffusion and oxygen permeability. In a murine subcutaneous implantation model, membranes are stable for 60 days and exhibit significantly reduced fibrous capsules, with enhanced vascularization near the membrane. These porous polymeric membranes can potentially be used as pro‐angiogenic immunoprotective membranes for cell transplantation applications where maximizing cell viability and function is of critical importance.

Engineering↗
DOE OSTI2026

Online task-space motion control for positioner-coordinated multi-robot manufacturing systems

Incorporating multiple robotic manipulators into large-scale manufacturing systems enhances production efficiency and expands manufacturing capabilities beyond those of single-robot systems. Workpiece positioners in robotic manufacturing have demonstrated significant benefits for process optimization, but coordination strategies for multi-robot systems with shared positioners have received limited attention. This work presents a task-space coordinated trajectory-tracking control framework for multi-robot manufacturing systems, in which robots coordinate their motions within a shared, dynamic workpiece positioning frame. A workpiece positioner actively adjusts the pose of the manufactured component to enable greater operational concurrency and improve overall production efficiency. The proposed motion-coordination scheme employs a distributed and scalable architecture, supporting coordination across heterogeneous multi-robot systems. Two optimization methodologies are introduced to manage kinematic redundancies and maintain continuous, near-optimal operation throughout the manufacturing process. The first strategy exploits a task-space dimensionality reduction to achieve locally optimal configurations by leveraging symmetry-axis rotations of the tool. The second strategy utilizes the workpiece positioner to drive the coordinated robots toward stable and kinematically favorable configurations. For both optimization strategies, multiple objectives are defined to improve key performance metrics, including manipulability, configuration consistency, proximity to mechanical limits, and motion efficiency. Addressing a key limitation of existing coordination approaches, the framework is designed around online setpoint modification, allowing coordinated robots to respond effectively to in-situ process feedback. The proposed control framework is validated using the Robot Operating System (ROS) middleware on a combination of physical and simulated multi-robot system hardware.

Arbogast, Alex [ORNL] (ORCID:0000000154740723)↗
DOE OSTI2025

Manufacturing of Continuous Microporous Polymeric Fibers for Separation Applications

Polyolefin plastic waste, particularly polypropylene, is one of the most prevalent components in the plastic waste stream generated globally. However, only a small fraction of this waste is recycled and reintegrated into second applications. These materials have significant embedded energy that could be used for additional productivity if recycled properly. Therefore, there is a critical need to develop scalable processing techniques with high throughput to effectively recycle it. We report the continuous manufacturing of microporous fibers using waste polypropylene plastic and melt-processable lignin, a plant biomass constituent produced as a byproduct in paper mills or biorefineries, as a template to create pores. Further, these filaments with hierarchical porosity─created by controlled microphase separation during high-speed extensional flow followed by removal of lignin─exhibit exceptional capillary action for hydrophobic liquids. The resulting porous fibers can be used for various separation applications. For example, the oil uptake of the fibers is 9.59 ± 0.82 g/g for applications in oil recovery in bodies of water. In addition, polyethylenimine infiltration within these nanoporous fibers introduces cyclic room temperature sorption and thermal desorption potential of acidic gases such as CO 2 in a bench-scale experiment. The fibers exhibit multiple cycles of CO 2 absorption, with a range of 0.15 to 0.17 mmol/g. Thus, these polyethylenimine-infiltered nanoporous polypropylene fibers can also be used in removing acidic gases from a gas mixture.

36 MATERIALS SCIENCE↗
DOE OSTI2025

ChatHPC: Building the Foundations for a Productive and Trustworthy AI-Assisted HPC Ecosystem

ChatHPC democratizes large language models for the high-performance computing (HPC) community by providing the infrastructure, ecosystem, and knowledge needed to apply modern generative AI technologies to rapidly create specific capabilities for critical HPC components while using relatively modest computational resources. Our divide-and-conquer approach focuses on creating a collection of reliable, highly specialized, and optimized AI assistants for HPC based on the cost-effective and fast Code Llama fine-tuning processes and expert supervision. We target major components of the HPC software stack, including programming models, runtimes, I/O, tooling, and math libraries. Thanks to AI, ChatHPC provides a more productive HPC ecosystem by boosting important tasks related to portability, parallelization, optimization, scalability, and instrumentation, among others. With relatively small datasets (on the order of KB), the AI assistants, which are created in a few minutes by using one node with two NVIDIA H100 GPUs and the ChatHPC library, can create new capabilities with Meta’s 7-billion parameter Code Llama base model to produce high-quality software with a level of trustworthiness of up to 90% higher than the 1.8-trillion parameter OpenAI ChatGPT-4o model for critical programming tasks in the HPC software stack.

Young, Aaron [ORNL] (ORCID:0000000254484667)↗